Privacy-Preserving Machine Learning Techniques
Table Of Contents
- <p>
- 1.Introduction<br>
- 1.1Background and Motivation<br>
- 1.2Objectives and Scope<br>
- 2.Literature Review<br>
- 2.1Privacy-Preserving Machine Learning Techniques<br>
- 2.2Trade-offs Between Privacy and Model Accuracy<br>
- 3.Federated Learning<br>
- 3.1Distributed Model Training and Aggregation<br>
- 3.2Privacy and Security Considerations<br>
- 4.Differential Privacy<br>
- 4.1Privacy Guarantees and Noise Addition<br>
- 4.2Privacy Budget and Utility Trade-offs<br>
- 5.Secure Multi-Party Computation<br>
- 5.1Secure Protocols for Collaborative Model Training<br>
- 5.2Privacy-Preserving Data Analysis<br></p>
Project Abstract
<p> Privacy-preserving machine learning techniques aim to enable the training and deployment of machine learning models while protecting sensitive data. This project will investigate various privacy-preserving methods, including federated learning, differential privacy, and secure multi-party computation. The project will focus on evaluating the trade-offs between privacy and model accuracy, as well as the practical considerations for implementing privacy-preserving machine learning in real-world applications. <br></p>Project Overview
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